极化散射矩阵及其测量

引言无论雷达使用或设计人员都十分关心目标的雷达截面积。它与雷达作用距离及测量误差关系很密切。早期,人们视目标为点目标,对目标回波作标量处理。随着雷达技术的发展以及目标分类、识别等要求的提出,人们对雷达截面积作了更精细的研究,诸如回波相位特性;幅、相起伏特性及谱分析;宽带探测条件下的回波响应等等。目标极化特性也是其中一个主要研究对象。     

基于小波变换与粗糙集的雷达目标识别方法

雷达目标识别是防空武器系统雷达信息处理的一个关键环节.在小波变换与粗糙集基础上提出一种雷达目标识别方法.小波变换能够提高了时--频分频率;粗糙集理论是一种新型的处理不确定性知识的数学工具.利用小波变换对目标原始信息进行分解,得到目标的能量特征向量;通过粗糙集简化关系表,删去冗余信息,用逻辑推理算法表示判别规则.应用小波变换与粗糙集能够满足利用不精确信息进行目标识别的需要.     

基于小波变换的单脉冲雷达目标RCS测量方法研究

随着现代信号处理技术的发展,对非平稳信号分析和处理的小波分析技术已成功应用于雷达目标特性分析领域,大功率单脉冲雷达作为我国航天测控网的主干设备,具有一定的目标特性识别能力。本文主要针对脉冲雷达RCS测量原理,讨论了基于小波变换的单脉冲雷达空间目标RCS测量方法,提出应发挥窄带低分辨率雷达在未来空间目标识别中的作用。     

导弹攻击机机载雷达的选择

机载雷达是导弹攻击机火力控制系统的重要组成部分。它可使飞机在昼夜和复杂气象条件下具有独立作战的能力。导弹是一种远航程的武器,飞机借助雷达能远距离发现、跟踪目标之特性,及早发现、识别目标,占据有利战术位置,发射导弹,对目标进行攻击。 本文根据发射导弹的技术要求,对雷达的主要战术技术指标,雷达在飞机攻击过程中的战术使用,国内雷达的技术性能比较、选型等方面进行讨论。     

雷达目标散射截面的计算

对雷达探测对象——空中飞行目标的特性研究,一直是雷达技术中一个非常重要的研究课题。雷达目标的特性研究,对雷达的设计、雷达目标的设计,以及雷达目标的特性分析和识别都具有十分重要的意义。目标的雷达散射截面(RCS)是雷达目标特性研究中一个最基本的参数。本文旨在介绍目标雷达散射截面的概念、理论分析和常用计算方法。     

毫米波宽带成像测量雷达及其关键技术研究

针对国内毫米波宽带成像测量雷达的建设思路,从3个方面展开论述:首先,列举了国外相关雷达的主要技术指标,并对其技术特点进行分析,通过采用高功率发射机及功率合成技术、低损耗传输技术、低噪声接收机技术等,国外毫米波雷达实现了对远距离目标的探测,具备很高的测距测角精度、cm级的距离分辨率和极高的多普勒灵敏度,具有较强的目标识别能力;随后,通过对单脉冲机械跟踪和相控阵2种体制的优缺点进行比对分析,建议国内应采用单脉冲机械跟踪体制,并重点探讨了引导捕获和宽带测量方案;最后,对宽带大功率发射机、波束波导天馈线系统、宽带超导接收机以及宽带数据采集等关键技术及其国内基础进行了分析,并给出了发展建议。     

机场探鸟雷达技术发展与应用综述

探鸟雷达已成为机场鸟击防范中重要的鸟情观测工具。首先，在介绍探鸟雷达技术起源的基础上，分析了目标回波幅度、飞行速度、飞行高度、轨迹特征、微动特征等飞鸟目标特性。然后，介绍了Merlin雷达、Accipiter雷达、Robin雷达以及Aveillant雷达等四种典型的机场探鸟雷达系统及国内的探鸟雷达技术研究现状，并分析了天线、雷达波形、目标检测与跟踪、目标识别与分类等雷达关键技术，进而对典型探鸟雷达系统的性能指标做对比分析。最后，从雷达与光电技术融合、探鸟与驱鸟联动、鸟情信息分析等方面讨论了探鸟雷达的应用情况，并做出结论与展望。     

空间目标探测与识别雷达工作频段与体制选择探讨

根据空间目标探测与识别雷达的任务要求,通过对雷达搜索、扫描、探测、多目标处理能力、分辨率的技术分析与经济性比较,给出了空间目标探测与识别雷达工作频段与体制选择的参考性结论。     

高频区雷达目标识别

随着雷达技术的发展,雷达目标识别日益受到广泛的重视.文中综述了高频区雷达目标识别的方法亟需解决的一些问题.     

基于模型的雷达系统仿真技术研究

介绍了通用仿真技术中的模型建立与模型校验技术。从雷达设备仿真、雷达信号模拟和雷达环境仿真三个方面讨论了雷达系统仿真中研究对象的性质与模型建立的基本方法,着重说明目标模型与平面杂波模型的主要类型、建立方法和研究动态。指出了模型校验在雷达系统仿真应用中的重要性,说明了系统仿真试验应用方法及对仿真的意义。     

Improved target classification using optimum polarimetric SARsignatures

We present a new method for automatic target/object classification by using the optimum polarimetric radar signatures of the targets/objects of interest. The state-of-the-art in radar target recognition is based mostly either on the use of single polarimetric pairs or on the four preset pairs of orthogonal polarimetric signatures. Due to these limitations, polarimetric radar processing has been fruitful only in the area of noise suppression and target detection. The use of target separability criteria for the optimal selection of radar signal state of polarizations is addressed here. The polarization scattering matrix is used for the derivation of target signatures at arbitrary transmit and receive polarization states (arbitrary polarization inclination angles and ellipticity angles). Then, an optimization criterion that minimizes the within-class distance and maximizes the between-class metrics is used for the derivation of optimum sets of polarimetric states. The results of the application of this approach on real synthetic aperture radar (SAR) data of military vehicles are obtained. The results show that noticeable improvements in target separability and consequently target classification can be achieved by the use of the optimum over nonoptimum signatures     

Superresolution HRR ATR with high definition vector imaging

A new 1-D template-based automatic target recognition (ATR) algorithm is developed and tested on high range resolution (HRR) profiles formed from synthetic aperture radar (SAR) images of targets taken from the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. In this work, a superresolution technique known as High Definition Vector Imaging (HDVI) is applied to the HRR profiles before the profiles are passed through ATR classification. The new I-D ATR system using HDVI demonstrates significantly improved target recognition compared with previous I-D ATR systems that use conventional image processing techniques. This improvement in target recognition is quantified by improvement in probability of correct classification (PCC). More importantly, the application of HDVI to HRR profiles helps to maintain the same ATR performance with reduced radar resource requirements     

GMM-based target classification for ground surveillance Doppler radar

An automatic target recognition (ATR) algorithm, based on greedy learning of Gaussian mixture model (GMM) is developed. The GMMs were obtained for a wide range of ground surveillance radar targets such as walking person(s), tracked or wheeled vehicles, animals, and clutter. Maximum-likelihood (ML) and majority-voting decision schemes were applied to these models for target classification. The corresponding classifiers were trained and tested using distinct databases of target echoes, recorded by ground surveillance radar. ML and majority-voting classifiers obtained classification rates of 88% and 96%, correspondingly. Both classifiers outperform trained human operators.     

Surface movement radar data processing methods for airportsurveillance

An airport surveillance function operating on surface movement radar (SMR) images is proposed and evaluated. The main contributions presented are the statistical error models of the target centroid and attributes extracted from radar images, developed and applied to the design of its main data processing blocks. Besides a multihypothesis image-to-tracks assignment method, a tracking filter using the extracted orientation and a classification scheme based on target attributes is detailed. The error models confidence and processing methods performance are demonstrated through simulation in representative scenarios     

Efficient ATR using compression

We examine various model-based automatic target recognition (MBATR) classifiers to investigate the utility of model-catalog compression realized via signal-vector quantization (VQ) and feature extraction. We specifically investigate the impact of various compression rates and common automatic target recognition (ATR) scenario variations such as noise and occlusion through simulations on high-range resolution (HRR) radar and synthetic aperture radar (SAR) data. For this data, we show that significant computational savings are possible for modest decreases in classification performance.     

Pulse Doppler signature of a rotary-wing aircraft

Field measurements of a modified Sikorsky S-55 helicopter target were carried out to investigate rotary-wing aircraft Doppler radar signature phenomenology. The results of the data analysis with regard to classification and identification of the aircraft based on its signature are presented. It was found that using the Doppler radar return and appropriate feature extraction techniques, the helicopter's design features can be estimated. Target backscatter from the main rotor blades, tail rotor blades, or hub can be used for target detection, acquisition, and classification as a rotary-wing aircraft. The extraction of configuration and blade count features can further define the helicopter for identification     

Radar target classification of commercial aircraft

With the increased availability of coherent wideband radars there has been a renewed interest in radar target recognition. A large bandwidth gives high resolution in range which means target discrimination may be possible. Coherence makes cross-range resolution and radar images possible. Some of the problems of classifying high resolution range profiles (HRRPs) are examined and simple preprocessing techniques which may aid subsequent target classification are investigated. These techniques are applied to HRRP data acquired at a local airport using the Microwave Radar Division (MRD) mobile radar facility It is found that Boeing 727 and Boeing 737 aircraft can be reliably distinguished over a range of aspect angles. This augers well for future target classification studies using HRRPs     

Softmax分类器深度学习图像分类方法应用综述

基于深度学习的人工智能图像分类方法研究是当前计算机视觉领域的研究热点。面向深度学习中的Softmax图像分类方法，首先回顾了图像分类技术的发展历程，接着介绍了图像识别技术中的分类器，并解释了Softmax回归函数的分类实现原理。基于Softmax回归分类器的应用，详细阐述了多种图像分类技术，具体包括浅层神经网络、深度置信网络、深度自编码器和卷积神经网络。同时，对比介绍了各种级联模型的具体结构、训练方法、实际应用、分类效果以及优缺点。最后，从Softmax回归分类器、深度学习网络模型和高维数据分类三个方面对基于Softmax回归分类器的深度学习模型在图像分类方面的发展与应用前景进行了展望。     

Bayesian gamma mixture model approach to radar target recognition

This paper develops a Bayesian gamma mixture model approach to automatic target recognition (ATR). The specific problem considered is the classification of radar range profiles (RRPs) of military ships. However, the approach developed is relevant to the generic discrimination problem. We model the radar returns (data measurements) from each target as a gamma mixture distribution. Several different motivations for the use of mixture models are put forward, with gamma components being chosen through a physical consideration of radar returns. Bayesian formalism is adopted and we obtain posterior distributions for the parameters of our mixture models. The distributions obtained are too complicated for direct analytical use in a classifier, so Markov chain Monte Carlo (MCMC) techniques are used to provide samples from the distributions. The classification results on the ship data compare favorably with those obtained from two previously published techniques, namely a self-organizing map and a maximum likelihood gamma mixture model classifier.